Bottom Line:
We found many proteins to be induced upon PLX4720 (BRAF inhibitor) treatment that are known to be involved in BRAF inhibitor resistance, including FOXD3 and ErbB3.Several proteins were down-regulated, including Rnd3, a negative regulator of ROCK1 kinase.For our genomic approach, we performed two parallel shRNA screens using a kinome library to identify genes whose inhibition sensitizes to BRAF or ERK inhibitor treatment.

fig02: MS analysis identifies proteins potentially contributing to melanoma survivalVolcano plots of protein expression levels at 1 day (left panel) and 3 days (right panel) compared to control sample. Statistically significant entries with a P-value < 0.05 and fold change ≥ 1.5 and ≤ −1.5 are labeled in green (one-sample t-test against 0). For simplicity, only some proteins have been tagged. ntot, nUP and nDOWN indicate the total number of proteins quantified in the three biological replicates and the number of statistically significant proteins that are up-regulated and down-regulated in the three biological replicates, respectively.

Mentions:
For the proteome and phosphoproteome analyses, cells were lysed, digested by Lys-C/Trypsin, labeled using the dimethyl approach (Boersema et al, 2009), mixed 1:1:1 and fractionated by strong cation exchange (SCX) (Fig1A). For the phosphoproteome analysis, SCX fractions were subsequently subjected to phosphopeptide enrichment by Ti4+-IMAC (Zhou et al, 2013) and analyzed by LC/MS/MS on an LTQ-Orbitrap Velos or Elite using a data-dependent decision tree MS/MS method (ETD-IT or HCD). Here, the most suitable fragmentation technique is automatically selected (according to the charge and m/z) to enhance the number of phosphopeptide identifications (Frese et al, 2011). For the whole proteome analysis, the SCX fractions were analyzed on an Orbitrap Velos, Elite or Q-Exactive (Fig1A). Overall, ∼5,700 proteins and ∼11,500 phosphosites (80% with a location probability ≥ 75%) were identified from the three biological replicates with a protein and peptide FDR ≤ 1%. At the protein level, we quantified ∼3,800 proteins over all conditions and found 129, 406 and 313 proteins regulated significantly at 1 day/control, 3 days/control and 3 days/1 day, respectively, after performing a statistical assessment (P < 0.05), and choosing an arbitrary fold change cutoff of 1.5, corresponding to a total of 588 unique regulated proteins (Fig2; Supplementary Table S1). Network analysis of the significantly changing proteins using Reactome as plugin in Cytoscape (Haw et al, 2011) revealed a high number of regulated protein–protein interactions at 3 days/control and a substantial up-regulation of membrane proteins at 3 days/1 day (Supplementary Fig S1). A gene ontology (GO) analysis using Panther (http://www.pantherdb.org) (Mi,2013) was performed on the proteins whose expression changed significantly. The most profound changes in protein expression levels were observed at 3 days, when receptors became particularly over-represented (Supplementary Fig S2; Supplementary Table S2). A GO slim analysis of the proteome and phosphoproteome data using BiNGO as Cytoscape plugin (Maere, 2005) revealed enrichment for cytoskeleton organization (Supplementary Fig S3; Supplementary Table S3).

fig02: MS analysis identifies proteins potentially contributing to melanoma survivalVolcano plots of protein expression levels at 1 day (left panel) and 3 days (right panel) compared to control sample. Statistically significant entries with a P-value < 0.05 and fold change ≥ 1.5 and ≤ −1.5 are labeled in green (one-sample t-test against 0). For simplicity, only some proteins have been tagged. ntot, nUP and nDOWN indicate the total number of proteins quantified in the three biological replicates and the number of statistically significant proteins that are up-regulated and down-regulated in the three biological replicates, respectively.

Mentions:
For the proteome and phosphoproteome analyses, cells were lysed, digested by Lys-C/Trypsin, labeled using the dimethyl approach (Boersema et al, 2009), mixed 1:1:1 and fractionated by strong cation exchange (SCX) (Fig1A). For the phosphoproteome analysis, SCX fractions were subsequently subjected to phosphopeptide enrichment by Ti4+-IMAC (Zhou et al, 2013) and analyzed by LC/MS/MS on an LTQ-Orbitrap Velos or Elite using a data-dependent decision tree MS/MS method (ETD-IT or HCD). Here, the most suitable fragmentation technique is automatically selected (according to the charge and m/z) to enhance the number of phosphopeptide identifications (Frese et al, 2011). For the whole proteome analysis, the SCX fractions were analyzed on an Orbitrap Velos, Elite or Q-Exactive (Fig1A). Overall, ∼5,700 proteins and ∼11,500 phosphosites (80% with a location probability ≥ 75%) were identified from the three biological replicates with a protein and peptide FDR ≤ 1%. At the protein level, we quantified ∼3,800 proteins over all conditions and found 129, 406 and 313 proteins regulated significantly at 1 day/control, 3 days/control and 3 days/1 day, respectively, after performing a statistical assessment (P < 0.05), and choosing an arbitrary fold change cutoff of 1.5, corresponding to a total of 588 unique regulated proteins (Fig2; Supplementary Table S1). Network analysis of the significantly changing proteins using Reactome as plugin in Cytoscape (Haw et al, 2011) revealed a high number of regulated protein–protein interactions at 3 days/control and a substantial up-regulation of membrane proteins at 3 days/1 day (Supplementary Fig S1). A gene ontology (GO) analysis using Panther (http://www.pantherdb.org) (Mi,2013) was performed on the proteins whose expression changed significantly. The most profound changes in protein expression levels were observed at 3 days, when receptors became particularly over-represented (Supplementary Fig S2; Supplementary Table S2). A GO slim analysis of the proteome and phosphoproteome data using BiNGO as Cytoscape plugin (Maere, 2005) revealed enrichment for cytoskeleton organization (Supplementary Fig S3; Supplementary Table S3).

Bottom Line:
We found many proteins to be induced upon PLX4720 (BRAF inhibitor) treatment that are known to be involved in BRAF inhibitor resistance, including FOXD3 and ErbB3.Several proteins were down-regulated, including Rnd3, a negative regulator of ROCK1 kinase.For our genomic approach, we performed two parallel shRNA screens using a kinome library to identify genes whose inhibition sensitizes to BRAF or ERK inhibitor treatment.